Machine learningNetwork science

Multilayer Closeness Centrality

Multilayer closeness centrality extends the classical closeness centrality measure to networks that contain multiple types of relationships or interaction contexts (layers). Rather than treating each layer in isolation, it computes how quickly a node can reach all others by traversing any combination of available layers, revealing nodes that are structurally efficient connectors across the full network system.

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Sources

  1. Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI: 10.1093/comnet/cnu016
  2. Sole-Ribalta, A., De Domenico, M., Kouvaris, N. E., Diaz-Guilera, A., Gomez, S., & Arenas, A. (2013). Spectral properties of the Laplacian of multiplex networks. Physical Review E, 88(3), 032807. DOI: 10.1103/PhysRevE.88.032807

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Referenced by

ScholarGateMultilayer Closeness Centrality (Multilayer Closeness Centrality (Generalized Closeness for Multilayer Networks)). Retrieved 2026-06-04 from https://scholargate.app/tr/network-analysis/multilayer-closeness-centrality